Application of machine learning for drug–target interaction prediction
L Xu, X Ru, R Song - Frontiers in genetics, 2021 - frontiersin.org
Exploring drug–target interactions by biomedical experiments requires a lot of human,
financial, and material resources. To save time and cost to meet the needs of the present …
financial, and material resources. To save time and cost to meet the needs of the present …
A comprehensive review of the imbalance classification of protein post-translational modifications
Post-translational modifications (PTMs) play significant roles in regulating protein structure,
activity and function, and they are closely involved in various pathologies. Therefore, the …
activity and function, and they are closely involved in various pathologies. Therefore, the …
MK-FSVM-SVDD: a multiple kernel-based fuzzy SVM model for predicting DNA-binding proteins via support vector data description
Background: Detecting DNA-binding proteins (DBPs) based on biological and chemical
methods is time-consuming and expensive. Objective: In recent years, the rise of …
methods is time-consuming and expensive. Objective: In recent years, the rise of …
Identification of drug–target interactions via multiple kernel-based triple collaborative matrix factorization
Targeted drugs have been applied to the treatment of cancer on a large scale, and some
patients have certain therapeutic effects. It is a time-consuming task to detect drug–target …
patients have certain therapeutic effects. It is a time-consuming task to detect drug–target …
AttentionMGT-DTA: A multi-modal drug-target affinity prediction using graph transformer and attention mechanism
The accurate prediction of drug-target affinity (DTA) is a crucial step in drug discovery and
design. Traditional experiments are very expensive and time-consuming. Recently, deep …
design. Traditional experiments are very expensive and time-consuming. Recently, deep …
A geometric deep learning framework for drug repositioning over heterogeneous information networks
Drug repositioning (DR) is a promising strategy to discover new indicators of approved
drugs with artificial intelligence techniques, thus improving traditional drug discovery and …
drugs with artificial intelligence techniques, thus improving traditional drug discovery and …
CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach
Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced
formed by the reverse splicing mechanism. Increasing evidence shows that circular RNAs …
formed by the reverse splicing mechanism. Increasing evidence shows that circular RNAs …
NmRF: identification of multispecies RNA 2'-O-methylation modification sites from RNA sequences
C Ao, Q Zou, L Yu - Briefings in bioinformatics, 2022 - academic.oup.com
O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-
methyltransferase and involves replacing the H on the 2′-hydroxyl group with a methyl …
methyltransferase and involves replacing the H on the 2′-hydroxyl group with a methyl …
iTTCA-RF: a random forest predictor for tumor T cell antigens
S Jiao, Q Zou, H Guo, L Shi - Journal of translational medicine, 2021 - Springer
Background Cancer is one of the most serious diseases threatening human health. Cancer
immunotherapy represents the most promising treatment strategy due to its high efficacy and …
immunotherapy represents the most promising treatment strategy due to its high efficacy and …
Identification of drug-target interactions via multi-view graph regularized link propagation model
Diseases are usually caused by body's own defects protein or the functional structure of viral
proteins. Effective drugs can be combined with these proteins well and remove original …
proteins. Effective drugs can be combined with these proteins well and remove original …